Machine Learning Engineering: From Prototype to Production is the definitive guide for transitioning ML models from research to reliable, scalable systems. This comprehensive book covers the entire lifecycle—data engineering, reproducible experimentation, model deployment, MLOps infrastructure, monitoring, scaling, security, and compliance—with practical patterns and real-world case studies. Designed for data scientists, software engineers, and technical leaders, it provides the engineering principles, infrastructure knowledge, and organizational practices needed to build ML systems that deliver sustained business value. The book balances theoretical foundations with actionable implementation details, making it an essential resource for anyone serious about production ML.

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